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Municipal wastewater treatment plants (MWWTPs) are essential infrastructures in any urban context, but they may be considered as a potential source of greenhouse gas (GHG) emissions and should be coherent with European Union (EU) policy on energy efficiency. This study presents a sustainability evaluation of four Italian and Romanian MWWTPs in terms of energy efficiency and greenhouse gas emissions using Energy Performance and Carbon Emissions Assessment and Monitoring (ECAM) tool software. The obtained results indicated that biogas recovery improved energy performances, while the largest contributions in terms of GHG emissions were in all cases caused by energy consumption and methane produced during wastewater treatment. The Romanian plants exhibited higher GHG emissions, compared to the Italian plants, mainly because of the different values of national conversion factors for grid electricity (0.41 kg CO2/kWh for Italy and 1.07 kg CO2/kWh for Romania). Two scenarios aimed at enhancing the overall sustainability were hypothesized, based on increasing the serviced population or energy efficiency, achieving significant improvements. A sustainability assessment of MWWTPs should be adopted as a useful tool to help water utilities to introduce low-energy, low-carbon management practices as well as being useful for policy recommendations.

This work presents recent developments of algal bioreactors used for CO2 removal and the factors affecting the reactor performance. The main focus of the study is on light intensity and photoperiods. The role of algae in CO2 removal, types of algal species used in bioreactors and conventional types of bioreactors including tubular bioreactor, vertical airlift reactor, bubble column reactor, flat panel or plate reactor, stirred tank reactor and specific type bioreactors such as hollow fibre membrane and disk photobioreactors etc. are discussed in details with respect to utilization of light. The effects of light intensity, light incident, photoinhibition, light provision arrangements and photoperiod on the performance of algal bioreactors for CO2 removal are also discussed. Efficient operation of algal photobioreactors cannot be achieved without the improvement in the utilization of incident light intensity and photoperiods. The readers may find this article has a much broader significance as algae is not only limited to removal or sequestration of CO2 but also it is used in a number of commercial applications including in energy (biofuel), nutritional and food sectors.

Machine Learning (ML) techniques are now being used very successfully in predicting and supporting decisions in multiple areas such as environmental issues and land management. These techniques have also provided promising results in the field of natural hazard assessment and risk mapping. The aim of this work is to apply the Supervised ML technique to train a model able to classify a particular gravity-driven coastal hillslope geomorphic model (slope-over-wall) involving most of the soft rocks of Cilento (southern Italy). To train the model, only geometric data have been used, namely morphometric feature maps computed on a Digital Terrain Model (DTM) derived from Light Detection and Ranging (LiDAR) data. Morphometric maps were computed using third-order polynomials, so as to obtain products that best describe landforms. Not all morphometric parameters from literature were used to train the model, the most significant ones were chosen by applying the Neighborhood Component Analysis (NCA) method. Different models were trained and the main indicators derived from the confusion matrices were compared. The best results were obtained using the Weighted k-NN model (accuracy score = 75%). Analysis of the Receiver Operating Characteristic (ROC) curves also shows that the discriminating capacity of the test reached percentages higher than 95%. The model, resulting more accurate in the training area, will be extended to similar areas along the Tyrrhenian coastal land.

The European physical journal / C Particles and fields C 79(1), 20 (2019). doi:10.1140/epjc/s10052-018-6482-9 Published by Springer, Heidelberg